The Kullback-Leibler divergence (KL-divergence) provides a measure of how different a probability distribution, \(G\)  is from a second probability distribution, \(H\) , denoted by \(KL(G||H)\)  [1]}. If \(KL(G||H) = 0\) , then the probability distributions \(G\)  and \(H\)  are identical. The induced embedding output, \(\textbf {y}_i\) , represented by probability distribution, \(Q\) , is obtained by minimising the sum of all KL-divergence measures between \(Q\)  and the distributions of every data-view \(m = 1, \cdots , M\) . In other words, multi-SNE minimises the cost function given by equation (REF ).
\(C_{multi-SNE} = & \sum _m \sum _i w^{(m)} KL(P^{(m)}_i||Q_i) \nonumber \\= & \sum _m \sum _{i} \sum _{j} w^{(m)} p^{(m)}_{ij} \log \frac{p^{(m)}_{ij}}{q_{ij}}  , \) 
